Sangam: A Confluence of Knowledge Streams

Network properties of written human language

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dc.creator Masucci, AP
dc.creator Rodgers, GJ
dc.date 2006-10-23T15:23:31Z
dc.date 2006-10-23T15:23:31Z
dc.date 2006
dc.date.accessioned 2022-05-25T13:06:43Z
dc.date.available 2022-05-25T13:06:43Z
dc.identifier A.P. Masucci, G.J. Rodgers, Network properties of written human language, Physics Review E74: 026102, May 2006
dc.identifier https://bura.brunel.ac.uk/handle/2438/292
dc.identifier https://doi.org/10.1103/PhysRevE.74.026102
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/163642
dc.description We investigate the nature of written human language within the framework of complex network theory. In particular, we analyse the topology of Orwell's \textit{1984} focusing on the local properties of the network, such as the properties of the nearest neighbors and the clustering coefficient. We find a composite power law behavior for both the average nearest neighbor's degree and average clustering coefficient as a function of the vertex degree. This implies the existence of different functional classes of vertices. Furthermore we find that the second order vertex correlations are an essential component of the network architecture. To model our empirical results we extend a previously introduced model for language due to Dorogovtsev and Mendes. We propose an accelerated growing network model that contains three growth mechanisms: linear preferential attachment, local preferential attachment and the random growth of a pre-determined small finite subset of initial vertices. We find that with these elementary stochastic rules we are able to produce a network showing syntactic-like structures.
dc.format 2759567 bytes
dc.format application/pdf
dc.language en
dc.publisher American Physical Society
dc.relation http://dx.doi.org/10.1103/PhysRevE.74.026102
dc.subject Natural languages
dc.subject Large-scale systems
dc.subject Topology
dc.title Network properties of written human language
dc.type Preprint
dc.coverage 9


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